The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-
4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with …

Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

We study the design decision of publicly available instruction tuning methods, by
reproducing and breaking down the development of Flan 2022 (Chung et al., 2022) …

Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or
modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize …

Patient recruitment is challenging for clinical trials. We introduce TrialGPT, an end-to-end
framework for zero-shot patient-to-trial matching with large language models. TrialGPT …

Prompt engineering is a critical technique in the field of natural language processing that
involves designing and optimizing the prompts used to input information into models, aiming …

Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et
al., 2018) have dramatically improved performance for many natural language processing …

Inspired by the success of the General Language Understanding Evaluation benchmark, we
introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to …

Conventional task-and modality-specific artificial intelligence (AI) models are inflexible in
real-world deployment and maintenance for biomedicine. At the same time, the growing …

BERT-based architectures currently give state-of-the-art performance on many NLP tasks,
but little is known about the exact mechanisms that contribute to its success. In the current …